Evolutionary Algorithms: Theory and Applications

Evolutionary algorithms which model natural evolution processes have been successfully used for optimization. Theoretical explanations why and how the algorithms work have been less successful. In this paper evolutionary algorithms are considered as random search methods. The genetic operators mutation and recombination are evaluated according to the measure expected progress. Mutation is analyzed by Markov chains and probability theory. Recombination is investigated together with selection by methods of quantitative genetics. Furthermore it is shown that hillclimbing improves the eeciency of the search in ruggged landscapes. The theoretical results are used in two evolutionary algorithms-the parallel genetic algorithm PGA and the breeder genetic algorithm BGA. The PGA models natural evolution which self-organizes itself, the BGA models rational controlled evolution by a virtual breeder. The eeciency of both algorithms is shown with numerical examples. Results of a real world problem, the determination of binary sequences of low autocorrelation, are reported.

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